Wind energy prediction and monitoring with neural computation

Neurocomputing

Volumn 109, Issue , 2013, Pages 84-93

  Kramer, Oliver ^a   Gieseke, Fabian ^a   Satzger, Benjamin ^b  

^a UNIVERSITY OF OLDENBURG   (Germany)

^b VIENNA UNIVERSITY OF TECHNOLOGY   (Austria)

Author keywords

Dimension reduction; Self organizing feature maps; Support vector regression; Time series monitoring; Wind energy; Wind prediction

Indexed keywords

DIMENSION REDUCTION; DIMENSION REDUCTION TECHNIQUES; MONITORING TECHNIQUES; NATIONAL RENEWABLE ENERGY LABORATORY; NEURAL COMPUTATIONS; SELF-ORGANIZING FEATURE MAP; SUPPORT VECTOR REGRESSION (SVR); WIND PREDICTION;

CONFORMAL MAPPING; FORECASTING; SELF ORGANIZING MAPS;

WIND POWER;

ANALYTICAL PARAMETERS; ARTICLE; ARTIFICIAL NEURAL NETWORK; DATA ANALYSIS; ENERGY RESOURCE; ENERGY YIELD; FORECASTING; KERNEL METHOD; MACHINE LEARNING; MATHEMATICAL COMPUTING; MEASUREMENT ACCURACY; MONITORING; PRIORITY JOURNAL; RENEWABLE ENERGY; SUPPORT VECTOR MACHINE; SYSTEM ANALYSIS; TIME SERIES ANALYSIS; WIND; WIND GRID; WIND MONITORING; WIND POWER;

EID: 84875913363     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.07.029     Document Type: Article

References (41)

1. O. Kramer, F. Gieseke, Short-term wind energy forecasting using support vector regression, in: International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO), 2011, pp. 271-280.
2. O. Kramer, F. Gieseke, Analysis of wind energy time series with kernel methods and neural networks, in: The Seventh International Conference on Natural Computation (ICNC), Shanghai, 2011, pp. 2381-2385.
3. O. Kramer, T. Hein, Monitoring of multivariate wind resources with self-organizing maps and slow feature analysis, in: IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG), IEEE Press, Paris, 2011.
4. D. Lew, M. Milligan, G. Jordan, L. Freeman, N. Miller, K. Clark, R. Piwko, How do wind and solar power affect grid operations: the western wind and solar integration study, in: The Eighth International Workshop on Large Scale Integration of Wind Power and on Transmission Networks for Offshore Wind Farms, 2009.
5. C.W. Potter, D. Lew, J. McCaa, S. Cheng, S. Eichelberger, E. Grimit, Creating the dataset for the western wind and solar integration study (USA), in: The Seventh International Workshop on Large Scale Integration of Wind Power and on Transmission Networks for Offshore Wind Farms, 2008.
6. Parzen E. On the estimation of a probability density function and mode. Ann. Math. Stat. 1962, 33:1065-1076.
7. B.W. Silverman, Density Estimation for Statistics and Data Analysis, Monographs on Statistics and Applied Probability, vol. 26, Chapman and Hall, London, 1986.
8. M. Milligan, K. Porter, E. DeMeo, P. Denholm, H. Holttinen, B. Kirby, N. Mille, A. Mills, M. OMalley, M. Schuerger, L. Soder, 〈〉 (February). http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5233741&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5233741.
9. Vapnik V. The Nature of Statistical Learning Theory 1995, Springer, New York.
10. Suykens J.A.K., Vandewalle J. Least squares support vector machine classifiers. Neural Process. Lett. 1999, 9(3):293-300.
11. Schölkopf B., Smola A.J. Learning with Kernels. Support Vector Machines, Regularization, Optimization, and Beyond 2001, MIT Press, Cambridge, MA, USA.
12. Costa A., Crespo A., Navarro J., Lizcano G., Madsen H., Feitosa E. A review on the young history of the wind power short-term prediction. Renew. Sustain. Energy Rev. 2008, 12(6):1725-1744.
13. M. Negnevitsky, P. Mandal, A. Srivastava, Machine learning applications for load, price and wind power prediction in power systems, in: Intelligent System Applications to Power Systems (ISAP), 2009, pp. 1-6.
14. Shuhui P.G., Li S., Wunsch D.C., Ohair E.A., Giesselmann M.G. Using neural networks to estimate wind turbine. J. Guid. Control Dyn. 2001, 16(3):276-282.
15. Li G., Shi J., Zhou J. Bayesian adaptive combination of short-term wind speed forecasts from neural network models. Renew. Energy 2011, 36(1):352-359.
16. Mohandes M., Halawani T., Rehman S., Hussain A.A. Support vector machines for wind speed prediction. Renew. Energy 2004, 29(6):939-947.
17. J. Shi, Y. Yang, P. Wang, Y. Liu, S. Han, Genetic algorithm-piecewise support vector machine model for short term wind power prediction, in: Proceedings of the Eighth World Congress on Intelligent Control and Automation, 2010, pp. 2254-2258.
18. P. Zhao, J. Xia, Y. Dai, J. He, Wind speed prediction using support vector regression, in: Industrial Electronics and Applications (ICIEA), 2010, pp. 882-886.
19. Kusiak A., Li W. The prediction and diagnosis of wind turbine faults. Renew. Energy 2011, 36(1):16-23.
20. B. Schölkopf, R. Herbrich, A.J. Smola, A generalized representer theorem, in: D.P. Helmbold, B. Williamson (Eds.), Proceedings of the 14th Annual Conference on Computational Learning Theory, 2001, pp. 416-426.
21. Steinwart I., Christmann A. Support Vector Machines 2008, Springer-Verlag, New York, NY, USA.
22. A.J. Smola, B. Schölkopf, B. Scholkopf, A Tutorial on Support Vector Regression, 1998.
23. Yang X., Chen S., Chen B., Pan Z. Proximal support vector machine using local information. Neurocomputing 2009, 73(1-3):357-365.
24. Wang D., Zheng J., Zhou Y., Li J. A scalable support vector machine for distributed classification in ad hoc sensor networks. Neurocomputing 2010, 74(1-3):394-400.
25. C.-C. Chang, C.-J. Lin, LIBSVM: A Library for Support Vector Machines, Software Available At 〈〉, 2001. http://www.csie.ntu.edu.tw/cjlin/libsvm.
26. Evangelista P.F., Embrechts M.J., Szymanski B.K. Taming the curse of dimensionality in kernels and novelty detection. Applied Soft Computing Technologies. The Challenge of Complexity 2006, 431-444. Springer-Verlag. A. Abraham, B. de Baets, M. Kppen, B. Nickolay (Eds.).
27. Kohonen T. Self-Organizing Maps 2001, Springer.
28. Ritter H., Martinetz T., Schulten K. Neural Computation and Self-Organizing Maps. An Introduction 1992, Addison Wesley.
29. Corona F., Liitiäinen E., Lendasse A., Sassu L., Melis S., Baratti R. A SOM-based approach to estimating product properties from spectroscopic measurements. Neurocomputing 2009, 73(1-3):71-79.
30. Merlin P., Sorjamaa A., Maillet B., Lendasse A. X-SOM and l-SOM. a double classification approach for missing value imputation. Neurocomputing 2010, 73(7-9):1103-1108.
31. D. Yu, Y. Qi, Y.-H. Xu, J.-Y. Yang, Kernel-SOM based visualization of financial time series forecasting, in: ICICIC, vol. 2, 2006, pp. 470-473.
32. P. Lehtimaeki, K. Raivio, A SOM based approach for visualization of gsm network performance data, in: Proceedings of the 18th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, 2005, pp. 588-598.
33. T. Hein, O. Kramer, Recognition and visualization of music sequences using self-organizing feature maps, in: KI, 2010, pp. 160-167.
34. O. Kramer, B. Satzger, J. Lässig, Power prediction in smart grids with evolutionary local kernel regression, in: HAIS, vol. 1, 2010, pp. 262-269.
35. Alhoniemi E., Hollmen J., Simula O., Vesanto J. Process monitoring and modeling using the self-organizing map. Integrated Comput. Aided Eng. 1999, 6:3-14.
36. T. Martinetz, K. Schulten, A neural gas network learns topologies, in: Artificial Neural Networks, Elsevier, 1991, pp. 397-402.
37. Jolliffe I. Principal Component Analysis, Springer Series in Statistics 1986, Springer, New York, USA.
38. Hastie T., Tibshirani R., Friedman J. The Elements of Statistical Learning 2009, Springer, Berlin.
39. Roweis S.T., Saul L.K. Nonlinear dimensionality reduction by locally linear embedding. Science 2000, 290:2323-2326.
40. Tenenbaum J.B., Silva V.D., Langford J.C. A global geometric framework for nonlinear dimensionality reduction. Science 2000, 290:2319-2323.
41. Diamantaras K.I., Papadimitriou T. Applying pca neural models for the blind separation of signals. Neurocomputing 2009, 73(1-3):3-9.